Inferring One-Way Arbitrage from Anonymous Crypto Exchange Trades

Summary

This study presents a method for identifying likely one-way arbitrage (OWA) sequences in spot trade records from centralized cryptocurrency exchanges, where public data lacks trader identities or wallet addresses. The approach infers linked trading sequences from anonymized trades, allowing researchers to estimate how often this activity occurs and its aggregate volume and profits.

Applied to five years of Binance data and nine years of Kraken data, the method identifies hundreds of millions of likely sequences on Binance and nearly two million on Kraken. Estimated total profits are positive, but average profit per sequence is below one dollar after fees. The study also reports that sequences became faster over time as their individual profitability declined. These are inferred patterns rather than directly identified traders, and the abstract does not detail the detection rules or uncertainty around the estimates. The results indicate recurring price discrepancies, but do not establish which market conditions make particular sequences profitable.

Key ideas

• Anonymous spot trades can be analyzed to infer likely one-way arbitrage sequences.
• The study applies its method to Binance and Kraken trade histories.
• Aggregate estimated profits coexist with very small average profit per sequence after fees.
• Inferred sequences became faster while their individual profitability declined.
• The precise conditions supporting profitable arbitrage remain unresolved.

Tags

# A Truckload of Satoshis: Detecting and Measuring One-Way Arbitrage in the Wild


# A Truckload of Satoshis: Detecting and Measuring One-Way Arbitrage in the Wild









Centralized cryptocurrency exchanges (CEXes) enable fast off-chain conversions between hundreds of coins. It is an open question which algorithmic trading patterns occur on these platforms. A major challenge to measuring CEXes is that their public trade data does not contain addresses or trader identifiers allowing linkage. We propose a novel methodology to infer one-way arbitrage (OWA) trading in anonymized spot trade data from CEXes. We identify 402 M likely OWA sequences in 5 years of trading on Binance (and almost 2 M during 9 years on Kraken), accounting for 0.94 % and 0.13 % of the total traded volume, respectively. While we estimate total profits of $31.2 M on Binance and $975 k on Kraken, profits from individual OWA sequences are less than $1 on average after accounting for trading fees. We also observe that OWA has become faster over time, while the profitability of individual sequences has decreased. Our findings highlight that pricing discrepancies regularly occur in CEXes, and raise questions for future work to identify the precise circumstances that enable profitable OWA.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.